Package GPfit. R topics documented: April 2, Title Gaussian Processes Modeling Version Date
|
|
- Tracy Patterson
- 5 years ago
- Views:
Transcription
1 Title Gaussian Processes Modeling Version Date Package GPfit April 2, 2015 Author Blake MacDoanld, Hugh Chipman, Pritam Ranjan Maintainer Hugh Chipman Description A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator. Imports lhs (>= 0.5), lattice (>= ) License GPL-2 NeedsCompilation no Repository CRAN Date/Publication :44:08 R topics documented: GPfit-package corr_matrix GP_deviance GP_fit plot.gp predict.gp print.gp summary.gp Index 17 1
2 2 corr_matrix GPfit-package Gaussian Process Modeling Description Details A computationally stable approach of fitting a Gaussian process (GP) model to simulator outputs. It is assumed that the input variables are continuous and the outputs are obtained from scalar valued deterministic computer simulator. This package implements a slightly modified version of the regularized GP model proposed in Ranjan et al. (2011). For details see MacDonald et al. (2015). A new parameterization of the Gaussian correlation is used for the ease of optimization. This package uses a multi-start gradient based search algorithm for optimizing the deviance (negative 2*log-likelihood). For a complete list of functions, use library(help="gpfit"). The main function for fitting the GP model is GP_fit. Author(s) Blake MacDoanld, Hugh Chipman, Pritam Ranjan Maintainer: Hugh Chipman <hugh.chipman@acadiau.ca> References MacDonald, K.B., Ranjan, P. and Chipman, H. (2015). GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs. Journal of Statistical Software, 64(12), Ranjan, P., Haynes, R., and Karsten, R. (2011). A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data, Technometrics, 53(4), Santner, T.J., Williams, B., and Notz, W. (2003), The design and analysis of computer experiments, Springer Verlag, New York. corr_matrix Power Exponential or Matern Correlation Matrix Description Computes the power exponential or Matern correlation matrix for a set of n design points in d- dimensional input region and a vector of d correlation hyper-parameters.
3 corr_matrix 3 Usage corr_matrix(x, beta, corr=list(type="exponential",power=1.95)) Arguments X the (n x d) design matrix beta a (d x 1) vector of correlation hyper-parameters in (, ) corr Details a list that specifies the type of correlation function along with the smoothness parameter. The default corresponds to power exponential correlation with smoothness parameter "power=1.95". One can specify a different power (between 1.0 and 2.0) for the power exponential, or use the Matern correlation function, specified as corr=list(type = "matern", nu=(2*k+1)/2), where k {0, 1, 2,... The power exponential correlation function is given by R ij = d k=1 exp( 10β k x ik x jk power ). The Matern correlation function given by Santner, Williams, and Notz (2003) is R ij = d 1 k=1 Γ(ν)2 (2 ν x ν 1 ik x jk 10 β k ) ν κ ν (2 ν x ik x jk 10 β k ), where κ ν is the modified Bessel function of order ν. Value Note The (n x n) correlation matrix, R, for the design matrix (X) and the hyper-parameters (beta). Both Matern and power exponential correlation functions use the new β parametrization of hyperparameters given by θ k = 10 β k for easier likelihood optimization. That is, beta is a log scale parameter (see MacDonald et al. (2015)). Author(s) Blake MacDonald, Hugh Chipman, Pritam Ranjan References MacDonald, K.B., Ranjan, P. and Chipman, H. (2015). GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs. Journal of Statistical Software, 64(12), Ranjan, P., Haynes, R., and Karsten, R. (2011). A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data, Technometrics, 53(4),
4 4 GP_deviance Santner, T.J., Williams, B., and Notz, W. (2003), The design and analysis of computer experiments, Springer Verlag, New York. Examples ## 1D Example - 1 n = 5; d = 1; set.seed(3); beta = rnorm(1); R = corr_matrix(x,beta); print(r) ## 1D Example - 2 beta = rnorm(1) R = corr_matrix(x,beta,corr = list(type = "matern")) print(r) ## 2D example - 1 n = 10; d = 2; set.seed(2); beta = rnorm(2); R = corr_matrix(x,beta,corr = list(type = "exponential", power = 2)); print(r) ## 2D example - 2 beta = rnorm(2) R = corr_matrix(x,beta,corr = list(type = "matern", nu = 5/2)) print(r) GP_deviance Computes the Deviance of a GP model Description Evaluates the deviance (negative 2*log-likelihood), as defined in Ranjan et al. (2011), however the correlation is reparametrized and can be either power exponential or Matern as discussed in corr_matrix. Usage GP_deviance(beta, X, Y, nug_thres = 20, corr = list(type="exponential",power=1.95))
5 GP_deviance 5 Arguments beta X Y nug_thres corr a (d x 1) vector of correlation hyper-parameters, as described in corr_matrix the (n x d) design matrix the (n x 1) vector of simulator outputs a parameter used in computing the nugget. See GP_fit. a list of parameters for the specifing the correlation to be used. See corr_matrix. Value the deviance (negative 2*log-likelihood) Author(s) Blake MacDonald, Hugh Chipman, Pritam Ranjan References Ranjan, P., Haynes, R., and Karsten, R. (2011). A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data, Technometrics, 53(4), See Also corr_matrix for computing the correlation matrix; GP_fit for estimating the parameters of the GP model. Examples ## 1D Example 1 n = 5; d = 1; computer_simulator <- function(x){ x = 2*x+0.5; y = sin(10*pi*x)/(2*x) + (x-1)^4; set.seed(3); beta = rnorm(1); GP_deviance(beta,x,y); ## 1D Example 2 n = 7; d = 1; y <- log(x+0.1)+sin(5*pi*x);
6 6 GP_fit beta = rnorm(1); GP_deviance(beta,x,y,corr=list(type="matern",nu=5/2)) ## 2D Example: GoldPrice Function x1=4*x[,1] - 2; x2=4*x[,2] - 2; t1 = 1 + (x1 + x2 + 1)^2*(19-14*x1 + 3*x1^2-14*x2 + 6*x1*x2 + 3*x2^2); t2 = 30 + (2*x1-3*x2)^2*(18-32*x1 + 12*x1^2 + 48*x2-36*x1*x2 + 27*x2^2); y = t1*t2; n = 10; d = 2; beta = rnorm(2); GP_deviance(beta,x,y); GP_fit Gaussian Process Model fitting Description Usage For an (n x d) design matrix, X, and the corresponding (n x 1) simulator output Y, this function fits the GP model and returns the parameter estimates. The optimization routine assumes that the inputs are scaled to the unit hypercube [0, 1] d. GP_fit(X, Y, control = c(200*d, 80*d, 2*d), nug_thres = 20, trace = FALSE,maxit = 100,corr = list(type="exponential",power=1.95), optim_start = NULL) Arguments X Y control nug_thres trace the (n x d) design matrix the (n x 1) vector of simulator outputs. a vector of parameters used in the search for optimal beta. See Details. a parameter used in computing the nugget. See Details. logical, if TRUE, will provide information on the optim runs maxit the maximum number of iterations within optim, defaults to 100
7 GP_fit 7 corr optim_start a list of parameters for the specifing the correlation to be used. See corr_matrix. a matrix of potentially likely starting values for correlation hyperparameters for the optim runs, i.e., initial guess of the d-vector beta Details This function fits the following GP model, y(x) = µ + Z(x), x [0, 1] d, where Z(x) is a GP with mean 0, V ar(z(x i )) = σ 2, and Cov(Z(x i ), Z(x j )) = σ 2 R ij. Entries in covariance matrix R are determined by corr and parameterized by beta, a d-vector of parameters. For computational stability R 1 is replaced with R 1 δ lb, where R δlb = R+δ lb I and δ lb is the nugget parameter described in Ranjan et al. (2011). The parameter estimate beta is obtained by minimizing the deviance using a multi-start gradient based search (L-BFGS-B) algorithm. The starting points are selected using the k-means clustering algorithm on a large maximin LHD for values of beta, after discarding beta vectors with high deviance. The control parameter determines the quality of the starting points of the L-BFGS-B algoritm. control is a vector of three tunable parameters used in the deviance optimization algorithm. The default values correspond to choosing 2*d clusters (using k-means clustering algorithm) based on 80*d best points (smallest deviance, refer to GP_deviance) from a 200*d - point random maximin LHD in beta. One can change these values to balance the trade-off between computational cost and robustness of likelihood optimization (or prediction accuracy). For details see MacDonald et al. (2015). The nug_thres parameter is outlined in Ranjan et al. (2011) and is used in finding the lower bound on the nugget (δ lb ). Value an object of class GP containing parameter estimates beta and sig2, nugget parameter delta, the data (X and Y), and a specification of the correlation structure used. Author(s) Blake MacDonald, Hugh Chipman, Pritam Ranjan References MacDonald, K.B., Ranjan, P. and Chipman, H. (2015). GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs. Journal of Statistical Software, 64(12), Ranjan, P., Haynes, R., and Karsten, R. (2011). A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data, Technometrics, 53(4), See Also plot.gp for plotting in 1 and 2 dimensions; predict.gp for predicting the response and error surfaces;
8 8 GP_fit optim for information on the L-BFGS-B procedure; GP_deviance for computing the deviance. Examples ## 1D Example 1 n = 5; d = 1; computer_simulator <- function(x){ x = 2*x+0.5; y = sin(10*pi*x)/(2*x) + (x-1)^4; set.seed(3); print(gpmodel) ## 1D Example 2 n = 7; d = 1; y <- log(x+0.1)+sin(5*pi*x); print(gpmodel, digits = 4) ## 2D Example: GoldPrice Function x1=4*x[,1] - 2; x2=4*x[,2] - 2; t1 = 1 + (x1 + x2 + 1)^2*(19-14*x1 + 3*x1^2-14*x2 + 6*x1*x2 + 3*x2^2); t2 = 30 + (2*x1-3*x2)^2*(18-32*x1 + 12*x1^2 + 48*x2-36*x1*x2 + 27*x2^2); y = t1*t2; n = 30; d = 2; print(gpmodel)
9 plot.gp 9 plot.gp Plotting GP model fits Description Usage Plots the predicted response and mean squared error (MSE) surfaces for simulators with 1 and 2 dimensional inputs (i.e. d = 1,2). ## S3 method for class GP plot(x, M = 1, range = c(0, 1), resolution = 50, colors = c( black, blue, red ), line_type = c(1, 2), pch = 20, cex = 1, legends = FALSE, surf_check = FALSE, response = TRUE,...) Arguments x M a class GP object estimated by GP_fit the number of iterations for use in prediction. See predict.gp range the input range for plotting (default set to [0, 1]) resolution the number of points along a coordinate in the specified range colors line_type pch cex legends For 1 Dimensional Plotting a vector of length 3 assigning colors[1] to training design points, colors[2] to model predictions, and colors[3] to the error bounds a vector of length 2 assigning line_type[1] to model predictions, and line_type[2] to the error bounds a parameter defining the plotting character for the training design points, see pch for possible options in par a parameter defining the size of the pch used for plotting the training design points, see cex for possible options in par a parameter that controls the inclusion of a legend; by default it is FALSE surf_check response For 2 Dimensional Plotting logical, switch between 3d surface and 2d level/contour plotting, the default of FALSE implies level/contour plotting logical, switch between predicted response and error (MSE) plots, the default of TRUE displays the response surface... additional arguments from wireframe or levelplot Author(s) Blake MacDonald, Hugh Chipman, Pritam Ranjan
10 10 plot.gp References Ranjan, P., Haynes, R., and Karsten, R. (2011). A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data, Technometrics, 53(4), See Also GP_fit for estimating the parameters of the GP model; predict.gp for predicting the response and error surfaces; par for additional plotting characters and line types for 1 dimensional plots; wireframe and levelplot for additional plotting settings in 2 dimensions. Examples ## 1D Example 1 n = 5; d = 1; computer_simulator <- function(x){ x = 2*x+0.5; y = sin(10*pi*x)/(2*x) + (x-1)^4; set.seed(3); plot.gp(gpmodel) ## 1D Example 2 n = 7; d = 1; y = log(x+0.1)+sin(5*pi*x); ## Plotting with changes from the default line type and characters plot.gp(gpmodel, resolution = 100, line_type = c(6,2), pch = 5) ## 2D Example: GoldPrice Function x1=4*x[,1] - 2; x2=4*x[,2] - 2; t1 = 1 + (x1 + x2 + 1)^2*(19-14*x1 + 3*x1^2-14*x2 + 6*x1*x2 + 3*x2^2); t2 = 30 + (2*x1-3*x2)^2*(18-32*x1 + 12*x1^2 + 48*x2-36*x1*x2 + 27*x2^2); y = t1*t2;
11 predict.gp 11 n = 30; d = 2; library(lattice); ## Basic level plot plot.gp(gpmodel) ## Adding Contours and increasing the number of levels plot.gp(gpmodel, contour = TRUE, cuts = 50, pretty = TRUE) ## Plotting the Response Surface plot.gp(gpmodel, surf_check = TRUE) ## Plotting the Error Surface with color plot.gp(gpmodel, surf_check = TRUE, response = FALSE, shade = TRUE) predict.gp Model Predictions from GPfit Description Usage Computes the regularized predicted response ŷ δlb,m (x) and the mean squared error s 2 δ lb,m (x) for a new set of inputs using the fitted GP model. ## S3 method for class GP predict(object, xnew = object$x,m=1,...) Arguments object xnew M Details Value a class GP object estimated by GP_fit the (n_new x d) design matrix of test points where model predictions and MSEs are desired the number of iterations. See Details... for compatibility with generic method predict The value of M determines the number of iterations (or terms) in approximating R 1 R 1 δ lb,m. The iterative use of the nugget δ lb, as outlined in Ranjan et al. (2011), is used in calculating ŷ δlb,m (x) and s 2 δ lb,m (x), where R 1 δ,m = M t=1 δt 1 (R + δi) t. Returns a list containing the predicted values (Y_hat), the mean squared errors of the predictions (MSE), and a matrix (complete_data) containing xnew, Y_hat, and MSE
12 12 predict.gp Author(s) Blake MacDonald, Hugh Chipman, Pritam Ranjan References Ranjan, P., Haynes, R., and Karsten, R. (2011). A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data, Technometrics, 53(4), See Also GP_fit for estimating the parameters of the GP model; plot.gp for plotting the predicted and error surfaces. Examples ## 1D Example n = 5; d = 1; computer_simulator <- function(x){ x = 2*x+0.5; y = sin(10*pi*x)/(2*x) + (x-1)^4; set.seed(3); xvec <- seq(from=0,to=1,length.out=10); GPprediction = predict.gp(gpmodel,xvec); yhat = GPprediction$Y_hat; mse = GPprediction$MSE; completedata = GPprediction$complete_data; completedata; ## 1D Example 2 n = 7; d = 1; y <- log(x+0.1)+sin(5*pi*x); set.seed(1) x = maximinlhs(n,d) y = computer_simulator(x) xvec <- seq(from=0,to=1,length.out=10); GPmodel = GP_fit(x,y) GPprediction = predict.gp(gpmodel,xvec); yhat = GPprediction$Y_hat; mse = GPprediction$MSE; completedata = GPprediction$complete_data;
13 print.gp 13 ## 2D Example: GoldPrice Function x1=4*x[,1] - 2; x2=4*x[,2] - 2; t1 = 1 + (x1 + x2 + 1)^2*(19-14*x1 + 3*x1^2-14*x2 + 6*x1*x2 + 3*x2^2); t2 = 30 + (2*x1-3*x2)^2*(18-32*x1 + 12*x1^2 + 48*x2-36*x1*x2 + 27*x2^2); y = t1*t2; n = 10; d = 2; xvector = seq(from=0,to=1,length.out=10); xvec = expand.grid(x = xvector, y=xvector); xvec = as.matrix(xvec); GPprediction = predict.gp(gpmodel,xvec); yhat = GPprediction$Y_hat; mse = GPprediction$MSE; completedata = GPprediction$complete_data; print.gp GP model fit Summary Description Prints the summary of a class GP object estimated by GP_fit Usage ## S3 method for class GP print(x,...) Arguments x Details a class GP object estimated by GP_fit... for compatibility with generic method print Prints the summary of the class GP object. It returns the number of observations, input dimension, parameter estimates of the GP model, lower bound on the nugget, and the nugget threshold parameter (described in GP_fit).
14 14 summary.gp Author(s) See Also Blake MacDonald, Hugh Chipman, Pritam Ranjan GP_fit for more information on estimating the model; print for more description on the print function. Examples ## 1D example n = 5; d = 1; computer_simulator <- function(x){ x = 2*x+0.5; y = sin(10*pi*x)/(2*x) + (x-1)^4; set.seed(3); print(gpmodel) ## 2D Example: GoldPrice Function x1=4*x[,1] - 2; x2=4*x[,2] - 2; t1 = 1 + (x1 + x2 + 1)^2*(19-14*x1 + 3*x1^2-14*x2 + 6*x1*x2 + 3*x2^2); t2 = 30 + (2*x1-3*x2)^2*(18-32*x1 + 12*x1^2 + 48*x2-36*x1*x2 + 27*x2^2); y = t1*t2; n = 10; d = 2; print(gpmodel,digits = 3) summary.gp Summary of GP model fit Description Prints the summary of a class GP object estimated by GP_fit
15 summary.gp 15 Usage ## S3 method for class GP summary(object,...) Arguments Details object a class GP object estimated by GP_fit... for compatibility with generic method summary prints the summary of the GP object (object), by calling print.gp Author(s) See Also Blake MacDonald, Hugh Chipman, Pritam Ranjan print.gp for more description of the output; GP_fit for more information on estimating the model; summary for more description on the summary function. Examples ## 1D example n = 5; d = 1; computer_simulator <- function(x){ x = 2*x+0.5; y = sin(10*pi*x)/(2*x) + (x-1)^4; set.seed(3); summary(gpmodel) ## 2D Example: GoldPrice Function x1=4*x[,1] - 2; x2=4*x[,2] - 2; t1 = 1 + (x1 + x2 + 1)^2*(19-14*x1 + 3*x1^2-14*x2 + 6*x1*x2 + 3*x2^2); t2 = 30 + (2*x1-3*x2)^2*(18-32*x1 + 12*x1^2 + 48*x2-36*x1*x2 + 27*x2^2); y = t1*t2; n = 10; d = 2;
16 16 summary.gp summary(gpmodel)
17 Index Topic Deviance GP_deviance, 4 Topic Gaussian Correlation corr_matrix, 2 Topic Gaussian Process Model Prediction predict.gp, 11 Topic Gaussian Process GP_fit, 6 Topic Matern Correlation corr_matrix, 2 Topic Power Exponential Correlation corr_matrix, 2 corr_matrix, 2, 4, 5, 7 GP_deviance, 4, 7, 8 GP_fit, 2, 5, 6, 10, GPfit (GPfit-package), 2 GPfit-package, 2 legend, 9 levelplot, 9, 10 optim, 6 8 par, 9, 10 plot.gp, 7, 9, 12 predict, 11 predict.gp, 7, 9, 10, 11 print, 13, 14 print.gp, 13, 15 summary, 15 summary.gp, 14 wireframe, 9, 10 17
Package effectfusion
Package November 29, 2016 Title Bayesian Effect Fusion for Categorical Predictors Version 1.0 Date 2016-11-21 Author Daniela Pauger [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut] Maintainer
More informationPackage darts. February 19, 2015
Type Package Package darts February 19, 2015 Title Statistical Tools to Analyze Your Darts Game Version 1.0 Date 2011-01-17 Author Maintainer Are you aiming at the right spot in darts?
More informationPackage alabama. March 6, 2015
Package alabama March 6, 2015 Type Package Title Constrained Nonlinear Optimization Description Augmented Lagrangian Adaptive Barrier Minimization Algorithm for optimizing smooth nonlinear objective functions
More informationPackage netcoh. May 2, 2016
Type Package Title Statistical Modeling with Network Cohesion Version 0.2 Date 2016-04-29 Author Tianxi Li, Elizaveta Levina, Ji Zhu Maintainer Tianxi Li Package netcoh May 2, 2016
More informationPackage NonpModelCheck
Type Package Package NonpModelCheck April 27, 2017 Title Model Checking and Variable Selection in Nonparametric Regression Version 3.0 Date 2017-04-27 Author Adriano Zanin Zambom Maintainer Adriano Zanin
More informationPackage spcov. R topics documented: February 20, 2015
Package spcov February 20, 2015 Type Package Title Sparse Estimation of a Covariance Matrix Version 1.01 Date 2012-03-04 Author Jacob Bien and Rob Tibshirani Maintainer Jacob Bien Description
More informationPackage ProbForecastGOP
Type Package Package ProbForecastGOP February 19, 2015 Title Probabilistic weather forecast using the GOP method Version 1.3.2 Date 2010-05-31 Author Veronica J. Berrocal , Yulia
More informationPackage scio. February 20, 2015
Title Sparse Column-wise Inverse Operator Version 0.6.1 Author Xi (Rossi) LUO and Weidong Liu Package scio February 20, 2015 Maintainer Xi (Rossi) LUO Suggests clime, lorec, QUIC
More informationPackage SpatPCA. R topics documented: February 20, Type Package
Type Package Package SpatPCA February 20, 2018 Title Regularized Principal Component Analysis for Spatial Data Version 1.2.0.0 Date 2018-02-20 URL https://github.com/egpivo/spatpca BugReports https://github.com/egpivo/spatpca/issues
More informationPackage convospat. November 3, 2017
Type Package Package convospat November 3, 2017 Title Convolution-Based Nonstationary Spatial Modeling Version 1.2.4 Date 2017-11-03 Fits convolution-based nonstationary Gaussian process models to point-referenced
More informationPackage gma. September 19, 2017
Type Package Title Granger Mediation Analysis Version 1.0 Date 2018-08-23 Package gma September 19, 2017 Author Yi Zhao , Xi Luo Maintainer Yi Zhao
More informationPackage msir. R topics documented: April 7, Type Package Version Date Title Model-Based Sliced Inverse Regression
Type Package Version 1.3.1 Date 2016-04-07 Title Model-Based Sliced Inverse Regression Package April 7, 2016 An R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.
More informationPackage rnmf. February 20, 2015
Type Package Title Robust Nonnegative Matrix Factorization Package rnmf February 20, 2015 An implementation of robust nonnegative matrix factorization (rnmf). The rnmf algorithm decomposes a nonnegative
More informationPackage MonoPoly. December 20, 2017
Type Package Title Functions to Fit Monotone Polynomials Version 0.3-9 Date 2017-12-20 Package MonoPoly December 20, 2017 Functions for fitting monotone polynomials to data. Detailed discussion of the
More informationThe ProbForecastGOP Package
The ProbForecastGOP Package April 24, 2006 Title Probabilistic Weather Field Forecast using the GOP method Version 1.3 Author Yulia Gel, Adrian E. Raftery, Tilmann Gneiting, Veronica J. Berrocal Description
More informationPackage flexcwm. R topics documented: July 11, Type Package. Title Flexible Cluster-Weighted Modeling. Version 1.0.
Package flexcwm July 11, 2013 Type Package Title Flexible Cluster-Weighted Modeling Version 1.0 Date 2013-02-17 Author Incarbone G., Mazza A., Punzo A., Ingrassia S. Maintainer Angelo Mazza
More informationPackage leiv. R topics documented: February 20, Version Type Package
Version 2.0-7 Type Package Package leiv February 20, 2015 Title Bivariate Linear Errors-In-Variables Estimation Date 2015-01-11 Maintainer David Leonard Depends R (>= 2.9.0)
More informationPackage clogitboost. R topics documented: December 21, 2015
Package December 21, 2015 Type Package Title Boosting Conditional Logit Model Version 1.1 Date 2015-12-09 Author Haolun Shi and Guosheng Yin Maintainer A set of functions to fit a boosting conditional
More informationPackage ICGOR. January 13, 2017
Package ICGOR January 13, 2017 Type Package Title Fit Generalized Odds Rate Hazards Model with Interval Censored Data Version 2.0 Date 2017-01-12 Author Jie Zhou, Jiajia Zhang, Wenbin Lu Maintainer Jie
More informationPackage fwsim. October 5, 2018
Version 0.3.4 Title Fisher-Wright Population Simulation Package fwsim October 5, 2018 Author Mikkel Meyer Andersen and Poul Svante Eriksen Maintainer Mikkel Meyer Andersen Description
More informationPackage condmixt. R topics documented: February 19, Type Package
Type Package Package condmixt February 19, 2015 Title Conditional Density Estimation with Neural Network Conditional Mixtures Version 1.0 Date 2012-04-06 Author Maintainer
More informationPackage pearson7. June 22, 2016
Version 1.0-2 Date 2016-06-21 Package pearson7 June 22, 2016 Title Maximum Likelihood Inference for the Pearson VII Distribution with Shape Parameter 3/2 Author John Hughes Maintainer John Hughes
More informationPackage spatial.gev.bma
Type Package Package spatial.gev.bma February 20, 2015 Title Hierarchical spatial generalized extreme value (GEV) modeling with Bayesian Model Averaging (BMA) Version 1.0 Date 2014-03-11 Author Alex Lenkoski
More informationPackage flexcwm. R topics documented: July 2, Type Package. Title Flexible Cluster-Weighted Modeling. Version 1.1.
Package flexcwm July 2, 2014 Type Package Title Flexible Cluster-Weighted Modeling Version 1.1 Date 2013-11-03 Author Mazza A., Punzo A., Ingrassia S. Maintainer Angelo Mazza Description
More informationPackage IsingFit. September 7, 2016
Type Package Package IsingFit September 7, 2016 Title Fitting Ising Models Using the ELasso Method Version 0.3.1 Date 2016-9-6 Depends R (>= 3.0.0) Imports qgraph, Matrix, glmnet Suggests IsingSampler
More informationPackage basad. November 20, 2017
Package basad November 20, 2017 Type Package Title Bayesian Variable Selection with Shrinking and Diffusing Priors Version 0.2.0 Date 2017-11-15 Author Qingyan Xiang , Naveen Narisetty
More informationCovariance function estimation in Gaussian process regression
Covariance function estimation in Gaussian process regression François Bachoc Department of Statistics and Operations Research, University of Vienna WU Research Seminar - May 2015 François Bachoc Gaussian
More informationPackage EDR. September 14, 2016
Version 0.6-6 Date 2016-09-06 Package EDR September 14, 2016 Title Estimation of the Effective Dimension Reduction ('EDR') Space Author Joerg Polzehl Maintainer Joerg Polzehl
More informationPackage GORCure. January 13, 2017
Package GORCure January 13, 2017 Type Package Title Fit Generalized Odds Rate Mixture Cure Model with Interval Censored Data Version 2.0 Date 2017-01-12 Author Jie Zhou, Jiajia Zhang, Wenbin Lu Maintainer
More informationPackage sklarsomega. May 24, 2018
Type Package Package sklarsomega May 24, 2018 Title Measuring Agreement Using Sklar's Omega Coefficient Version 1.0 Date 2018-05-22 Author John Hughes Maintainer John Hughes
More informationPackage skellam. R topics documented: December 15, Version Date
Version 0.2.0 Date 2016-12-13 Package skellam December 15, 2016 Title Densities and Sampling for the Skellam Distribution Author Jerry W. Lewis, Patrick E. Brown , Michail Tsagris
More informationPackage SimSCRPiecewise
Package SimSCRPiecewise July 27, 2016 Type Package Title 'Simulates Univariate and Semi-Competing Risks Data Given Covariates and Piecewise Exponential Baseline Hazards' Version 0.1.1 Author Andrew G Chapple
More informationPackage covtest. R topics documented:
Package covtest February 19, 2015 Title Computes covariance test for adaptive linear modelling Version 1.02 Depends lars,glmnet,glmpath (>= 0.97),MASS Author Richard Lockhart, Jon Taylor, Ryan Tibshirani,
More informationPackage mhsmm. January 15, 2017
Type Package Package mhsmm January 15, 2017 Title Inference for Hidden Markov and Semi-Markov Models Version 0.4.16 Date 2017-01-15 Author Jared O'Connell , SÃ ren HÃ jsgaard
More informationPackage LPTime. March 3, 2015
Type Package Package LPTime March 3, 2015 Title LP Nonparametric Approach to Non-Gaussian Non-Linear Time Series Modelling Version 1.0-2 Date 2015-03-03 URL http://sites.temple.edu/deepstat/d-products/
More informationPackage elhmc. R topics documented: July 4, Type Package
Package elhmc July 4, 2017 Type Package Title Sampling from a Empirical Likelihood Bayesian Posterior of Parameters Using Hamiltonian Monte Carlo Version 1.1.0 Date 2017-07-03 Author Dang Trung Kien ,
More informationPackage CEC. R topics documented: August 29, Title Cross-Entropy Clustering Version Date
Title Cross-Entropy Clustering Version 0.9.4 Date 2016-04-23 Package CEC August 29, 2016 Author Konrad Kamieniecki [aut, cre], Przemyslaw Spurek [ctb] Maintainer Konrad Kamieniecki
More informationPackage ProDenICA. February 19, 2015
Type Package Package ProDenICA February 19, 2015 Title Product Density Estimation for ICA using tilted Gaussian density estimates Version 1.0 Date 2010-04-19 Author Trevor Hastie, Rob Tibshirani Maintainer
More informationPackage invgamma. May 7, 2017
Package invgamma May 7, 2017 Type Package Title The Inverse Gamma Distribution Version 1.1 URL https://github.com/dkahle/invgamma BugReports https://github.com/dkahle/invgamma/issues Description Light
More informationPackage esaddle. R topics documented: January 9, 2017
Package esaddle January 9, 2017 Type Package Title Extended Empirical Saddlepoint Density Approximation Version 0.0.3 Date 2017-01-07 Author Matteo Fasiolo and Simon Wood Maintainer Matteo Fasiolo
More informationPackage threeboost. February 20, 2015
Type Package Package threeboost February 20, 2015 Title Thresholded variable selection and prediction based on estimating equations Version 1.1 Date 2014-08-09 Author Julian Wolfson and Christopher Miller
More informationPackage ShrinkCovMat
Type Package Package ShrinkCovMat Title Shrinkage Covariance Matrix Estimators Version 1.2.0 Author July 11, 2017 Maintainer Provides nonparametric Steinian shrinkage estimators
More informationPackage TwoStepCLogit
Package TwoStepCLogit March 21, 2016 Type Package Title Conditional Logistic Regression: A Two-Step Estimation Method Version 1.2.5 Date 2016-03-19 Author Radu V. Craiu, Thierry Duchesne, Daniel Fortin
More informationPackage RTransferEntropy
Type Package Package RTransferEntropy March 12, 2019 Title Measuring Information Flow Between Time Series with Shannon and Renyi Transfer Entropy Version 0.2.8 Measuring information flow between time series
More informationPackage penmsm. R topics documented: February 20, Type Package
Type Package Package penmsm February 20, 2015 Title Estimating Regularized Multi-state Models Using L1 Penalties Version 0.99 Date 2015-01-12 Author Maintainer Structured fusion
More informationPackage mistwosources
Type Package Package mistwosources November 4, 2016 Title Probit models with two misclassified responses Version 1.0 Date 2016-10-07 Author Scott J. Cook, Betsabe Blas, Raymond J. Carroll and Samiran Sinha
More informationPackage gtheory. October 30, 2016
Package gtheory October 30, 2016 Version 0.1.2 Date 2016-10-22 Title Apply Generalizability Theory with R Depends lme4 Estimates variance components, generalizability coefficients, universe scores, and
More informationPackage BayesNI. February 19, 2015
Package BayesNI February 19, 2015 Type Package Title BayesNI: Bayesian Testing Procedure for Noninferiority with Binary Endpoints Version 0.1 Date 2011-11-11 Author Sujit K Ghosh, Muhtarjan Osman Maintainer
More informationPackage sscor. January 28, 2016
Type Package Package sscor January 28, 2016 Title Robust Correlation Estimation and Testing Based on Spatial Signs Version 0.2 Date 2016-01-19 Depends pcapp, robustbase, mvtnorm Provides the spatial sign
More informationPackage EMVS. April 24, 2018
Type Package Package EMVS April 24, 2018 Title The Expectation-Maximization Approach to Bayesian Variable Selection Version 1.0 Date 2018-04-23 Author Veronika Rockova [aut,cre], Gemma Moran [aut] Maintainer
More informationPackage monmlp. R topics documented: December 5, Type Package
Type Package Package monmlp December 5, 2017 Title Multi-Layer Perceptron Neural Network with Optional Monotonicity Constraints Version 1.1.5 Author Ale J. Cannon Maintainer Ale J. Cannon
More informationPackage beam. May 3, 2018
Type Package Package beam May 3, 2018 Title Fast Bayesian Inference in Large Gaussian Graphical Models Version 1.0.2 Date 2018-05-03 Author Gwenael G.R. Leday [cre, aut], Ilaria Speranza [aut], Harry Gray
More informationPackage lomb. February 20, 2015
Package lomb February 20, 2015 Type Package Title Lomb-Scargle Periodogram Version 1.0 Date 2013-10-16 Author Thomas Ruf, partially based on C original by Press et al. (Numerical Recipes) Maintainer Thomas
More informationPackage rbvs. R topics documented: August 29, Type Package Title Ranking-Based Variable Selection Version 1.0.
Type Package Title Ranking-Based Variable Selection Version 1.0.2 Date 2015-12-08 Package rbvs August 29, 2016 Author Rafal Baranowski, Patrick Breheny, Isaac Turner Maintainer Rafal Baranowski
More informationPackage eel. September 1, 2015
Type Package Title Etended Empirical Likelihood Version 1.1 Date 2015-08-30 Author Fan Wu and Yu Zhang Maintainer Yu Zhang Package eel September 1, 2015 Compute the etended empirical log
More informationPackage CorrMixed. R topics documented: August 4, Type Package
Type Package Package CorrMixed August 4, 2016 Title Estimate Correlations Between Repeatedly Measured Endpoints (E.g., Reliability) Based on Linear Mixed-Effects Models Version 0.1-13 Date 2015-03-08 Author
More informationPackage CompQuadForm
Type Package Package CompQuadForm April 12, 2017 Title Distribution Function of Quadratic Forms in Normal Variables Version 1.4.3 Date 2017-04-10 Author P. Lafaye de Micheaux Maintainer P. Lafaye de Micheaux
More informationPackage depth.plot. December 20, 2015
Package depth.plot December 20, 2015 Type Package Title Multivariate Analogy of Quantiles Version 0.1 Date 2015-12-19 Maintainer Could be used to obtain spatial depths, spatial ranks and outliers of multivariate
More informationPackage factorqr. R topics documented: February 19, 2015
Package factorqr February 19, 2015 Version 0.1-4 Date 2010-09-28 Title Bayesian quantile regression factor models Author Lane Burgette , Maintainer Lane Burgette
More informationPackage FDRSeg. September 20, 2017
Type Package Package FDRSeg September 20, 2017 Title FDR-Control in Multiscale Change-Point Segmentation Version 1.0-3 Date 2017-09-20 Author Housen Li [aut], Hannes Sieling [aut], Timo Aspelmeier [cre]
More informationmlegp: an R package for Gaussian process modeling and sensitivity analysis
mlegp: an R package for Gaussian process modeling and sensitivity analysis Garrett Dancik January 30, 2018 1 mlegp: an overview Gaussian processes (GPs) are commonly used as surrogate statistical models
More informationKriging models with Gaussian processes - covariance function estimation and impact of spatial sampling
Kriging models with Gaussian processes - covariance function estimation and impact of spatial sampling François Bachoc former PhD advisor: Josselin Garnier former CEA advisor: Jean-Marc Martinez Department
More informationPackage idmtpreg. February 27, 2018
Type Package Package idmtpreg February 27, 2018 Title Regression Model for Progressive Illness Death Data Version 1.1 Date 2018-02-23 Author Leyla Azarang and Manuel Oviedo de la Fuente Maintainer Leyla
More informationPackage blme. August 29, 2016
Version 1.0-4 Date 2015-06-13 Title Bayesian Linear Mixed-Effects Models Author Vincent Dorie Maintainer Vincent Dorie Package blme August 29, 2016 Description Maximum a posteriori
More informationPackage icmm. October 12, 2017
Type Package Package icmm October 12, 2017 Title Empirical Bayes Variable Selection via ICM/M Algorithm Version 1.1 Author Vitara Pungpapong [aut, cre], Min Zhang [aut], Dabao Zhang [aut] Maintainer Vitara
More informationPackage ICBayes. September 24, 2017
Package ICBayes September 24, 2017 Title Bayesian Semiparametric Models for Interval-Censored Data Version 1.1 Date 2017-9-24 Author Chun Pan, Bo Cai, Lianming Wang, and Xiaoyan Lin Maintainer Chun Pan
More informationPackage abundant. January 1, 2017
Type Package Package abundant January 1, 2017 Title High-Dimensional Principal Fitted Components and Abundant Regression Version 1.1 Date 2017-01-01 Author Adam J. Rothman Maintainer Adam J. Rothman
More informationGaussian Processes. Le Song. Machine Learning II: Advanced Topics CSE 8803ML, Spring 2012
Gaussian Processes Le Song Machine Learning II: Advanced Topics CSE 8803ML, Spring 01 Pictorial view of embedding distribution Transform the entire distribution to expected features Feature space Feature
More informationPackage horseshoe. November 8, 2016
Title Implementation of the Horseshoe Prior Version 0.1.0 Package horseshoe November 8, 2016 Description Contains functions for applying the horseshoe prior to highdimensional linear regression, yielding
More informationThe nltm Package. July 24, 2006
The nltm Package July 24, 2006 Version 1.2 Date 2006-07-17 Title Non-linear Transformation Models Author Gilda Garibotti, Alexander Tsodikov Maintainer Gilda Garibotti Depends
More informationPackage jmcm. November 25, 2017
Type Package Package jmcm November 25, 2017 Title Joint Mean-Covariance Models using 'Armadillo' and S4 Version 0.1.8.0 Maintainer Jianxin Pan Fit joint mean-covariance models
More informationPackage unbalhaar. February 20, 2015
Type Package Package unbalhaar February 20, 2015 Title Function estimation via Unbalanced Haar wavelets Version 2.0 Date 2010-08-09 Author Maintainer The package implements top-down
More informationPackage dhsic. R topics documented: July 27, 2017
Package dhsic July 27, 2017 Type Package Title Independence Testing via Hilbert Schmidt Independence Criterion Version 2.0 Date 2017-07-24 Author Niklas Pfister and Jonas Peters Maintainer Niklas Pfister
More informationPackage bivariate. December 10, 2018
Title Bivariate Probability Distributions Version 0.3.0 Date 2018-12-10 License GPL (>= 2) Package bivariate December 10, 2018 Maintainer Abby Spurdle Author Abby Spurdle URL https://sites.google.com/site/asrpws
More informationPackage Blendstat. February 21, 2018
Package Blendstat February 21, 2018 Type Package Title Joint Analysis of Experiments with Mixtures and Random Effects Version 1.0.0 Date 2018-02-20 Author Marcelo Angelo Cirillo Paulo
More informationPackage face. January 19, 2018
Package face January 19, 2018 Title Fast Covariance Estimation for Sparse Functional Data Version 0.1-4 Author Luo Xiao [aut,cre], Cai Li [aut], William Checkley [aut], Ciprian Crainiceanu [aut] Maintainer
More informationPackage alphaoutlier
Type Package Package Outlier September 9, 2016 Title Obtain Alpha-Outlier Regions for Well-Known Probability Distributions Version 1.2.0 Date 2016-09-09 Author Andre Rehage, Sonja Kuhnt Maintainer Andre
More informationPackage Grace. R topics documented: April 9, Type Package
Type Package Package Grace April 9, 2017 Title Graph-Constrained Estimation and Hypothesis Tests Version 0.5.3 Date 2017-4-8 Author Sen Zhao Maintainer Sen Zhao Description Use
More informationPackage NORMT3. February 19, 2015
Package NORMT3 February 19, 2015 Title Evaluates complex erf, erfc, Faddeeva, and density of sum of Gaussian and Student's t Version 1.0-3 Date 31/10/2012 Author Guy Nason Maintainer
More informationPackage peacots. November 13, 2016
Type Package Package peacots November 13, 2016 Title Periodogram Peaks in Correlated Time Series Version 1.3 Date 2016-11-12 Author Stilianos Louca Maintainer Stilianos Louca Description
More informationPackage pssm. March 1, 2017
Version 1.1 Type Package Date 2017-02-28 Package pssm March 1, 2017 Title Piecewise Exponential Model for Time to Progression and Time from Progression to Death Author David A. Schoenfeld [aut, cre] Maintainer
More informationPackage covsep. May 6, 2018
Package covsep May 6, 2018 Title Tests for Determining if the Covariance Structure of 2-Dimensional Data is Separable Version 1.1.0 Author Shahin Tavakoli [aut, cre], Davide Pigoli [ctb], John Aston [ctb]
More informationPackage univoutl. January 9, 2018
Type Package Title Detection of Univariate Outliers Version 0.1-4 Date 2017-12-27 Author Marcello D'Orazio Package univoutl January 9, 2018 Maintainer Marcello D'Orazio Depends
More informationPackage threg. August 10, 2015
Package threg August 10, 2015 Title Threshold Regression Version 1.0.3 Date 2015-08-10 Author Tao Xiao Maintainer Tao Xiao Depends R (>= 2.10), survival, Formula Fit a threshold regression
More informationPackage neuralnet. August 16, 2016
Type Package Title Training of Neural Networks Version 1.33 Date 2016-08-05 Package neuralnet August 16, 2016 Author Stefan Fritsch [aut], Frauke Guenther [aut, cre], Marc Suling [ctb], Sebastian M. Mueller
More informationPackage ARCensReg. September 11, 2016
Type Package Package ARCensReg September 11, 2016 Title Fitting Univariate Censored Linear Regression Model with Autoregressive Errors Version 2.1 Date 2016-09-10 Author Fernanda L. Schumacher, Victor
More informationPackage sbgcop. May 29, 2018
Package sbgcop May 29, 2018 Title Semiparametric Bayesian Gaussian Copula Estimation and Imputation Version 0.980 Date 2018-05-25 Author Maintainer Estimation and inference for parameters
More informationPackage R1magic. April 9, 2013
Package R1magic April 9, 2013 Type Package Title Compressive Sampling: Sparse signal recovery utilities Version 0.2 Date 2013-04-09 Author Maintainer Depends stats, utils Provides
More informationPackage CPE. R topics documented: February 19, 2015
Package CPE February 19, 2015 Title Concordance Probability Estimates in Survival Analysis Version 1.4.4 Depends R (>= 2.10.0),survival,rms Author Qianxing Mo, Mithat Gonen and Glenn Heller Maintainer
More informationPackage mpmcorrelogram
Type Package Package mpmcorrelogram Title Multivariate Partial Mantel Correlogram Version 0.1-4 Depends vegan Date 2017-11-17 Author Marcelino de la Cruz November 17, 2017 Maintainer Marcelino de la Cruz
More informationPackage gwqs. R topics documented: May 7, Type Package. Title Generalized Weighted Quantile Sum Regression. Version 1.1.0
Package gwqs May 7, 2018 Type Package Title Generalized Weighted Quantile Sum Regression Version 1.1.0 Author Stefano Renzetti, Paul Curtin, Allan C Just, Ghalib Bello, Chris Gennings Maintainer Stefano
More informationPackage ForwardSearch
Package ForwardSearch February 19, 2015 Type Package Title Forward Search using asymptotic theory Version 1.0 Date 2014-09-10 Author Bent Nielsen Maintainer Bent Nielsen
More informationPackage mmm. R topics documented: February 20, Type Package
Package mmm February 20, 2015 Type Package Title an R package for analyzing multivariate longitudinal data with multivariate marginal models Version 1.4 Date 2014-01-01 Author Ozgur Asar, Ozlem Ilk Depends
More informationPackage ForecastCombinations
Type Package Title Forecast Combinations Version 1.1 Date 2015-11-22 Author Eran Raviv Package ForecastCombinations Maintainer Eran Raviv November 23, 2015 Description Aim: Supports
More informationPackage BootWPTOS. R topics documented: June 30, Type Package
Type Package Package BootWPTOS June 30, 2016 Title Test Stationarity using Bootstrap Wavelet Packet Tests Version 1.2 Date 2016-06-30 Depends R (>= 2.0), wavethresh, fractal Description Provides significance
More informationPackage ltsbase. R topics documented: February 20, 2015
Package ltsbase February 20, 2015 Type Package Title Ridge and Liu Estimates based on LTS (Least Trimmed Squares) Method Version 1.0.1 Date 2013-08-02 Author Betul Kan Kilinc [aut, cre], Ozlem Alpu [aut,
More informationPackage FDRreg. August 29, 2016
Package FDRreg August 29, 2016 Type Package Title False discovery rate regression Version 0.1 Date 2014-02-24 Author James G. Scott, with contributions from Rob Kass and Jesse Windle Maintainer James G.
More informationPackage BSSasymp. R topics documented: September 12, Type Package
Type Package Package BSSasymp September 12, 2017 Title Asymptotic Covariance Matrices of Some BSS Mixing and Unmixing Matrix Estimates Version 1.2-1 Date 2017-09-11 Author Jari Miettinen, Klaus Nordhausen,
More informationPackage QuantifQuantile
Type Package Package QuantifQuantile August 13, 2015 Title Estimation of Conditional Quantiles using Optimal Quantization Version 2.2 Date 2015-08-13 Author Isabelle Charlier and Davy Paindaveine and Jerome
More informationPackage shrink. August 29, 2013
Package shrink August 29, 2013 Type Package Title Global, Parameterwise, and Joint Shrinkage of Regression Coefficients Version 1.0 Date 2013-04-03 Author Daniela Dunkler, Georg Heinze Maintainer Daniela
More information